Code
# install.packages("gganimate")
# install.packages("gifski")
library(tidyverse)
library(lubridate)
library(gganimate)
library(gifski)
theme_set(theme_bw())Lab 7
Setup
Code
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
destfile = "data/time_series_covid19_confirmed_global.csv")Code
time_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv")|>
rename(Province_State = "Province/State", Country_Region = "Country/Region")Examples
Code
time_series_confirmed_long <- time_series_confirmed |>
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
time_series_confirmed_long$Date <- mdy(time_series_confirmed_long$Date)
time_series_confirmed_long|>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Confirmed)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Confirmed Cases")
Code
time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) |>
ggplot(aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
Code
time_series_confirmed_long_daily <-time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
mutate(Daily = Confirmed - lag(Confirmed, default = first(Confirmed )))
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("COVID-19 Confirmed Cases")
Code
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
Code
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth() +
ggtitle("COVID-19 Confirmed Cases")
Code
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth(method = "gam", se = FALSE) +
ggtitle("COVID-19 Confirmed Cases")
Code
daily_counts <- time_series_confirmed_long_daily |>
filter (Country_Region == "US")
p <- ggplot(daily_counts, aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("Confirmed COVID-19 Cases") +
# gganimate lines
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
Code
anim_save("daily_counts_US.gif", p)Code
# This download may take about 5 minutes. You only need to do this once so set `#| eval: false` in your qmd file
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "data/time_series_covid19_deaths_global.csv")Code
time_series_deaths_confirmed <- read_csv("data/time_series_covid19_deaths_global.csv")|>
rename(Province_State = "Province/State", Country_Region = "Country/Region")
time_series_deaths_long <- time_series_deaths_confirmed |>
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
time_series_deaths_long$Date <- mdy(time_series_deaths_long$Date)
p <- time_series_deaths_long |>
filter (Country_Region %in% c("US","Canada", "Mexico","Brazil","Egypt","Ecuador","India", "Netherlands", "Germany", "China" )) |>
ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) +
geom_point(aes(size=Confirmed)) +
transition_time(Date) +
labs(title = "Cumulative Deaths: {frame_time}") +
ylab("Deaths") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
Exercises
1
Examples
Code
library(tibble)
table1 <- tibble(
country = c("Afghanistan", "Afghanistan", "Brazil", "Brazil", "China", "China"),
year = c(1999, 2000, 1999, 2000, 1999, 2000),
cases = c(745, 2666, 37737, 80488, 212258, 213766),
population = c(19987071, 20595360, 172006362, 174504898, 1272915272, 1280428583)
)
library(tidyr)
table2 <- table1 %>%
pivot_longer(cols = c(cases, population),
names_to = "type",
values_to = "count")
table3 <- table1 %>%
mutate(rate = paste(cases, population, sep = "/")) %>%
select(country, year, rate)
table1# A tibble: 6 × 4
country year cases population
<chr> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071
2 Afghanistan 2000 2666 20595360
3 Brazil 1999 37737 172006362
4 Brazil 2000 80488 174504898
5 China 1999 212258 1272915272
6 China 2000 213766 1280428583Code
table2# A tibble: 12 × 4
country year type count
<chr> <dbl> <chr> <dbl>
1 Afghanistan 1999 cases 745
2 Afghanistan 1999 population 19987071
3 Afghanistan 2000 cases 2666
4 Afghanistan 2000 population 20595360
5 Brazil 1999 cases 37737
6 Brazil 1999 population 172006362
7 Brazil 2000 cases 80488
8 Brazil 2000 population 174504898
9 China 1999 cases 212258
10 China 1999 population 1272915272
11 China 2000 cases 213766
12 China 2000 population 1280428583Code
table3# A tibble: 6 × 3
country year rate
<chr> <dbl> <chr>
1 Afghanistan 1999 745/19987071
2 Afghanistan 2000 2666/20595360
3 Brazil 1999 37737/172006362
4 Brazil 2000 80488/174504898
5 China 1999 212258/1272915272
6 China 2000 213766/1280428583Code
# Compute rate per 10,000
table1 |>
mutate(rate = cases / population * 10000)# A tibble: 6 × 5
country year cases population rate
<chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071 0.373
2 Afghanistan 2000 2666 20595360 1.29
3 Brazil 1999 37737 172006362 2.19
4 Brazil 2000 80488 174504898 4.61
5 China 1999 212258 1272915272 1.67
6 China 2000 213766 1280428583 1.67 Code
# Compute total cases per year
table1 |>
group_by(year) |>
summarize(total_cases = sum(cases))# A tibble: 2 × 2
year total_cases
<dbl> <dbl>
1 1999 250740
2 2000 296920Code
# Visualize changes over time
ggplot(table1, aes(x = year, y = cases)) +
geom_line(aes(group = country), color = "grey50") +
geom_point(aes(color = country, shape = country)) +
scale_x_continuous(breaks = c(1999, 2000)) # x-axis breaks at 1999 and 2000
5.2.1
In table 1, each observation is a unique country/year pair. The columns are country, year, the number of TB cases, and the population of that country in that year to compare against the number of TB cases. In table 2, observations are changed to be each unique country/year/data type (population vs cases) combination, and while country and year remain as columns, the last two columns are changed to the data type (case or population) and the count for that data. In table 3, observations are identical to those in table 1, with the sole change being compressing the cases and population columns into a single rate column equal to the # of cases dividided by the population.
In table 2, I’d extract the number of cases per country per year from the count column (odd row numbers), divide this by the population per country per year also in the count column (even row numbers, specifically row i+1 where row i is where the # of cases is taken from) and multiply by 10,000. I would store these rates in new columns with type “rate”, such that each country/year pair would have 3 rows. (This would be an inefficient way to store the data, but consistent with the table’s design.) In table 3, I’d extract the “rate” column, convert the string to a numerical value (i.e. go from “745/19987071” to the value 745/19987071), multiply by 10,000, and mutate the table to add a new column “rate per 10,000” with this value.
Examples
Code
billboard# A tibble: 317 × 79
artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA
2 2Ge+her The … 2000-09-02 91 87 92 NA NA NA NA NA
3 3 Doors D… Kryp… 2000-04-08 81 70 68 67 66 57 54 53
4 3 Doors D… Loser 2000-10-21 76 76 72 69 67 65 55 59
5 504 Boyz Wobb… 2000-04-15 57 34 25 17 17 31 36 49
6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2
7 A*Teens Danc… 2000-07-08 97 97 96 95 100 NA NA NA
8 Aaliyah I Do… 2000-01-29 84 62 51 41 38 35 35 38
9 Aaliyah Try … 2000-03-18 59 53 38 28 21 18 16 14
10 Adams, Yo… Open… 2000-08-26 76 76 74 69 68 67 61 58
# ℹ 307 more rows
# ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>,
# wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>,
# wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>,
# wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>,
# wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>,
# wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, …Code
billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank"
)# A tibble: 24,092 × 5
artist track date.entered week rank
<chr> <chr> <date> <chr> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
8 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk8 NA
9 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk9 NA
10 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk10 NA
# ℹ 24,082 more rowsCode
billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
)# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <chr> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 wk2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 wk3 92
# ℹ 5,297 more rowsCode
billboard_longer <- billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
) |>
mutate(
week = parse_number(week)
)
billboard_longer# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <dbl> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 3 92
# ℹ 5,297 more rowsCode
billboard_longer |>
ggplot(aes(x = week, y = rank, group = track)) +
geom_line(alpha = 0.25) +
scale_y_reverse()
Code
df <- tribble(
~id, ~bp1, ~bp2,
"A", 100, 120,
"B", 140, 115,
"C", 120, 125
)
df |>
pivot_longer(
cols = bp1:bp2,
names_to = "measurement",
values_to = "value"
)# A tibble: 6 × 3
id measurement value
<chr> <chr> <dbl>
1 A bp1 100
2 A bp2 120
3 B bp1 140
4 B bp2 115
5 C bp1 120
6 C bp2 125Code
who2# A tibble: 7,240 × 58
country year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554 sp_m_5564
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1980 NA NA NA NA NA NA
2 Afghanistan 1981 NA NA NA NA NA NA
3 Afghanistan 1982 NA NA NA NA NA NA
4 Afghanistan 1983 NA NA NA NA NA NA
5 Afghanistan 1984 NA NA NA NA NA NA
6 Afghanistan 1985 NA NA NA NA NA NA
7 Afghanistan 1986 NA NA NA NA NA NA
8 Afghanistan 1987 NA NA NA NA NA NA
9 Afghanistan 1988 NA NA NA NA NA NA
10 Afghanistan 1989 NA NA NA NA NA NA
# ℹ 7,230 more rows
# ℹ 50 more variables: sp_m_65 <dbl>, sp_f_014 <dbl>, sp_f_1524 <dbl>,
# sp_f_2534 <dbl>, sp_f_3544 <dbl>, sp_f_4554 <dbl>, sp_f_5564 <dbl>,
# sp_f_65 <dbl>, sn_m_014 <dbl>, sn_m_1524 <dbl>, sn_m_2534 <dbl>,
# sn_m_3544 <dbl>, sn_m_4554 <dbl>, sn_m_5564 <dbl>, sn_m_65 <dbl>,
# sn_f_014 <dbl>, sn_f_1524 <dbl>, sn_f_2534 <dbl>, sn_f_3544 <dbl>,
# sn_f_4554 <dbl>, sn_f_5564 <dbl>, sn_f_65 <dbl>, ep_m_014 <dbl>, …Code
who2 |>
pivot_longer(
cols = !(country:year),
names_to = c("diagnosis", "gender", "age"),
names_sep = "_",
values_to = "count"
)# A tibble: 405,440 × 6
country year diagnosis gender age count
<chr> <dbl> <chr> <chr> <chr> <dbl>
1 Afghanistan 1980 sp m 014 NA
2 Afghanistan 1980 sp m 1524 NA
3 Afghanistan 1980 sp m 2534 NA
4 Afghanistan 1980 sp m 3544 NA
5 Afghanistan 1980 sp m 4554 NA
6 Afghanistan 1980 sp m 5564 NA
7 Afghanistan 1980 sp m 65 NA
8 Afghanistan 1980 sp f 014 NA
9 Afghanistan 1980 sp f 1524 NA
10 Afghanistan 1980 sp f 2534 NA
# ℹ 405,430 more rowsCode
household# A tibble: 5 × 5
family dob_child1 dob_child2 name_child1 name_child2
<int> <date> <date> <chr> <chr>
1 1 1998-11-26 2000-01-29 Susan Jose
2 2 1996-06-22 NA Mark <NA>
3 3 2002-07-11 2004-04-05 Sam Seth
4 4 2004-10-10 2009-08-27 Craig Khai
5 5 2000-12-05 2005-02-28 Parker Gracie Code
household |>
pivot_longer(
cols = !family,
names_to = c(".value", "child"),
names_sep = "_",
values_drop_na = TRUE
)# A tibble: 9 × 4
family child dob name
<int> <chr> <date> <chr>
1 1 child1 1998-11-26 Susan
2 1 child2 2000-01-29 Jose
3 2 child1 1996-06-22 Mark
4 3 child1 2002-07-11 Sam
5 3 child2 2004-04-05 Seth
6 4 child1 2004-10-10 Craig
7 4 child2 2009-08-27 Khai
8 5 child1 2000-12-05 Parker
9 5 child2 2005-02-28 GracieCode
cms_patient_experience# A tibble: 500 × 5
org_pac_id org_nm measure_cd measure_title prf_rate
<chr> <chr> <chr> <chr> <dbl>
1 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 63
2 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 87
3 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 86
4 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 57
5 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 85
6 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 24
7 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 59
8 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 85
9 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 83
10 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 63
# ℹ 490 more rowsCode
cms_patient_experience |>
pivot_wider(
names_from = measure_cd,
values_from = prf_rate
)# A tibble: 500 × 9
org_pac_id org_nm measure_title CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 0446157747 USC CARE MEDICA… CAHPS for MI… 63 NA NA
2 0446157747 USC CARE MEDICA… CAHPS for MI… NA 87 NA
3 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA 86
4 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
5 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
6 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
7 0446162697 ASSOCIATION OF … CAHPS for MI… 59 NA NA
8 0446162697 ASSOCIATION OF … CAHPS for MI… NA 85 NA
9 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA 83
10 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA NA
# ℹ 490 more rows
# ℹ 3 more variables: CAHPS_GRP_5 <dbl>, CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>Code
cms_patient_experience |>
pivot_wider(
id_cols = starts_with("org"),
names_from = measure_cd,
values_from = prf_rate
)# A tibble: 95 × 8
org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 CAHPS_GRP_8
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0446157747 USC C… 63 87 86 57 85
2 0446162697 ASSOC… 59 85 83 63 88
3 0547164295 BEAVE… 49 NA 75 44 73
4 0749333730 CAPE … 67 84 85 65 82
5 0840104360 ALLIA… 66 87 87 64 87
6 0840109864 REX H… 73 87 84 67 91
7 0840513552 SCL H… 58 83 76 58 78
8 0941545784 GRITM… 46 86 81 54 NA
9 1052612785 COMMU… 65 84 80 58 87
10 1254237779 OUR L… 61 NA NA 65 NA
# ℹ 85 more rows
# ℹ 1 more variable: CAHPS_GRP_12 <dbl>Code
df <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"B", "bp1", 140,
"B", "bp2", 115,
"A", "bp2", 120,
"A", "bp3", 105
)
df |>
pivot_wider(
names_from = measurement,
values_from = value
)# A tibble: 2 × 4
id bp1 bp2 bp3
<chr> <dbl> <dbl> <dbl>
1 A 100 120 105
2 B 140 115 NACode
df |>
distinct(measurement) |>
pull()[1] "bp1" "bp2" "bp3"Code
df |>
select(-measurement, -value) |>
distinct()# A tibble: 2 × 1
id
<chr>
1 A
2 B Code
df |>
select(-measurement, -value) |>
distinct() |>
mutate(x = NA, y = NA, z = NA)# A tibble: 2 × 4
id x y z
<chr> <lgl> <lgl> <lgl>
1 A NA NA NA
2 B NA NA NA Code
df <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"A", "bp1", 102,
"A", "bp2", 120,
"B", "bp1", 140,
"B", "bp2", 115
)
df |>
pivot_wider(
names_from = measurement,
values_from = value
)# A tibble: 2 × 3
id bp1 bp2
<chr> <list> <list>
1 A <dbl [2]> <dbl [1]>
2 B <dbl [1]> <dbl [1]>Code
df |>
group_by(id, measurement) |>
summarize(n = n(), .groups = "drop") |>
filter(n > 1)# A tibble: 1 × 3
id measurement n
<chr> <chr> <int>
1 A bp1 22
Code
time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed), .groups = "drop") |>
filter(Country_Region %in% c("China", "France", "Italy",
"Korea, South", "US")) |>
ggplot(aes(x = Date, y = Confirmed)) +
geom_point(color = "steelblue") +
geom_line(color = "steelblue") +
facet_wrap(~ Country_Region, scales = "free_y") +
ggtitle("COVID-19 Confirmed Cases by Country") +
theme_minimal()
3
Code
time_series_confirmed_long_daily |>
filter (Country_Region %in% c("US", "Canada", "Mexico", "Brazil", "Egypt")) |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth() +
ggtitle("COVID-19 Confirmed Cases")
4
Code
p <- time_series_deaths_long |>
filter (Country_Region %in% c("US","Canada", "Mexico")) |>
ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) +
geom_point(aes(size=Confirmed)) +
transition_time(Date) +
labs(title = "Cumulative Deaths: {frame_time}") +
ylab("Deaths") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
5
Code
time_series_confirmed_deaths_daily <- time_series_deaths_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
mutate(Daily = Confirmed - lag(Confirmed, default = first(Confirmed )))
daily_counts_deaths <- time_series_confirmed_deaths_daily |>
filter (Country_Region %in% c("US","Canada", "Mexico"))
ggplot(daily_counts_deaths, aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("Confirmed COVID-19 Deaths")
6
Code
graph <- time_series_confirmed_long_daily |>
filter (Country_Region == "US" |
Country_Region == "Brazil" |
Country_Region == "Canada" |
Country_Region == "China" |
Country_Region == "Egypt") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("Confirmed COVID-19 Cases") +
# gganimate lines
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
animate(graph, renderer = gifski_renderer(), end_pause = 15)